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Atlantic/Bermuda clock

search-query-len

Count characters in a search query. Query discarded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
refNoGit ref name; discarded after the shape check
urlNoHTTPS URL to normalize or cite
cityNoCity name for a public weather hint; discarded after the call
feedNoPublic RSS or Atom URL; titles discarded
hostNoPublic hostname
jsonNoJSON text to validate; discarded after the check
pathNoFile path to check; no disk access
zoneNoIANA timezone name
queryNoSearch text; discarded after the length check

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry the burden of behavioral disclosure. It reveals that the query is discarded (a non-destructive trait), but this is redundant with the schema descriptions for each parameter, which already state similar discarding behavior. The description does not disclose what the tool returns (e.g., an integer length) or whether any other side effects occur, leaving significant behavioral ambiguity for a tool that accepts nine parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise: two short sentences that front-load the action. It contains no filler or repetition. It is appropriately minimal for the stated purpose, though it may be too terse given the complexity of the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with nine parameters and no output schema, the description is incomplete. It fails to explain what the tool does with the other parameters, what output format to expect, or how it fits into the broader toolset. The minimal description leaves substantial gaps that an agent needs to make a correct call.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all nine parameters are already documented with their own descriptions, including the discarding behavior. The tool description adds no additional parameter meaning beyond what the schema provides. It specifically mentions 'search query,' which maps to the 'query' parameter, but does not explain the purpose of the other eight parameters or their relation to the tool's core function. This meets the baseline for full schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a clear verb and resource: count characters in a search query. It is specific enough to identify the core action, though it does not differentiate from sibling tools that also count lengths (e.g., memory-key-count, hn-front-count). The tool name itself is unambiguous, and the description reinforces it without being a tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives. The description gives no context for choosing it over sibling tools, nor does it mention any exclusions or conditions. An agent would have to infer usage solely from the name and minimal description.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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